class_docstr: Options for the holistic landmarker task.
  
  Attributes:
    base_options: Base options for the holistic landmarker task.
    running_mode: The running mode of the task. Default to the image mode.
      HolisticLandmarker has three running modes: 1) The image mode for
      detecting holistic landmarks on single image inputs. 2) The video mode for
      detecting holistic landmarks on the decoded frames of a video. 3) The live
      stream mode for detecting holistic landmarks on the live stream of input
      data, such as from camera. In this mode, the "result_callback" below must
      be specified to receive the detection results asynchronously.
    min_face_detection_confidence: The minimum confidence score for the face
      detection to be considered successful.
    min_face_suppression_threshold: The minimum non-maximum-suppression
      threshold for face detection to be considered overlapped.
    min_face_landmarks_confidence: The minimum confidence score for the face
      landmark detection to be considered successful.
    min_pose_detection_confidence: The minimum confidence score for the pose
      detection to be considered successful.
    min_pose_suppression_threshold: The minimum non-maximum-suppression
      threshold for pose detection to be considered overlapped.
    min_pose_landmarks_confidence: The minimum confidence score for the pose
      landmark detection to be considered successful.
    min_hand_landmarks_confidence: The minimum confidence score for the hand
      landmark detection to be considered successful.
    output_face_blendshapes: Whether HolisticLandmarker outputs face blendshapes
      classification. Face blendshapes are used for rendering the 3D face model.
    output_segmentation_mask: whether to output segmentation masks.
    result_callback: The user-defined result callback for processing live stream
      data. The result callback should only be specified when the running mode
      is set to the live stream mode.
docstr: Generates an HolisticLandmarkerGraphOptions protobuf object.
docstr_tok: ["Generates","an","HolisticLandmarkerGraphOptions","protobuf","object","."]
code: def to_pb2(self) -> _HolisticLandmarkerGraphOptionsProto:
      """Generates an HolisticLandmarkerGraphOptions protobuf object."""
      base_options_proto = self.base_options.to_pb2()
      base_options_proto.use_stream_mode = (
          False if self.running_mode == _RunningMode.IMAGE else True
      )
  
      # Initialize the holistic landmarker options from base options.
      holistic_landmarker_options_proto = (
          _HolisticLandmarkerGraphOptionsProto(
              base_options=base_options_proto
          )
      )
      # Configure face detector and face landmarks detector options.
      holistic_landmarker_options_proto.face_detector_graph_options.min_detection_confidence = (
          self.min_face_detection_confidence
      )
      holistic_landmarker_options_proto.face_detector_graph_options.min_suppression_threshold = (
          self.min_face_suppression_threshold
      )
      holistic_landmarker_options_proto.face_landmarks_detector_graph_options.min_detection_confidence = (
          self.min_face_landmarks_confidence
      )
      # Configure pose detector and pose landmarks detector options.
      holistic_landmarker_options_proto.pose_detector_graph_options.min_detection_confidence = (
          self.min_pose_detection_confidence
      )
      holistic_landmarker_options_proto.pose_detector_graph_options.min_suppression_threshold = (
          self.min_pose_suppression_threshold
      )
      holistic_landmarker_options_proto.pose_landmarks_detector_graph_options.min_detection_confidence = (
          self.min_pose_landmarks_confidence
      )
      # Configure hand landmarks detector options.
      holistic_landmarker_options_proto.hand_landmarks_detector_graph_options.min_detection_confidence = (
          self.min_hand_landmarks_confidence
      )
      return holistic_landmarker_options_proto
  
imports: import dataclasses
  from typing import Callable, List, Mapping, Optional
  from mediapipe.framework.formats import classification_pb2
  from mediapipe.framework.formats import landmark_pb2
  from mediapipe.python import packet_creator
  from mediapipe.python import packet_getter
  from mediapipe.python._framework_bindings import image
  from mediapipe.python._framework_bindings import packet
  from mediapipe.tasks.cc.vision.holistic_landmarker.proto import holistic_landmarker_graph_options_pb2
  from mediapipe.tasks.cc.vision.holistic_landmarker.proto import holistic_result_pb2
  from mediapipe.tasks.python.components.containers import category
  from mediapipe.tasks.python.components.containers import landmark
  from mediapipe.tasks.python.core import base_options
  from mediapipe.tasks.python.core import task_info
  from mediapipe.tasks.python.core.optional_dependencies import doc_controls
  from mediapipe.tasks.python.vision.core import base_vision_task_api
  from mediapipe.tasks.python.vision.core import image_processing_options
  from mediapipe.tasks.python.vision.core import vision_task_running_mode
label_desc: Use image node_id 1 for calling the HolisticLandmarkerOptions obj's underlying member method code with example usage: obj.to_pb2() and returns: holistic_landmarker_options_proto